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Background: Studies have shown an increased risk of severe SARS-CoV-2–related (COVID-19) disease outcome and mortality for patients with cancer, but it is not well understood whether associations vary by cancer site, cancer treatment, and vaccination status. Methods: Using electronic health record data from an academic medical center, we identified a retrospective cohort of 260,757 individuals tested for or diagnosed with COVID-19 from March 10, 2020, to August 1, 2022. Of these, 52,019 tested positive for COVID-19 of whom 13,752 had a cancer diagnosis. We conducted Firth-corrected logistic regression to assess the association between cancer status, site, treatment, vaccination, and four COVID-19 outcomes: hospitalization, intensive care unit admission, mortality, and a composite “severe COVID” outcome. Results: Cancer diagnosis was significantly associated with higher rates of severe COVID, hospitalization, and mortality. These associations were driven by patients whose most recent initial cancer diagnosis was within the past 3 years. Chemotherapy receipt, colorectal cancer, hematologic malignancies, kidney cancer, and lung cancer were significantly associated with higher rates of worse COVID-19 outcomes. Vaccinations were significantly associated with lower rates of worse COVID-19 outcomes regardless of cancer status. Conclusions: Patients with colorectal cancer, hematologic malignancies, kidney cancer, or lung cancer or who receive chemotherapy for treatment should be cautious because of their increased risk of worse COVID-19 outcomes, even after vaccination. Impact: Additional COVID-19 precautions are warranted for people with certain cancer types and treatments. Significant benefit from vaccination is noted for both cancer and cancer-free patients.

The presence of macroscopic residual disease after primary cytoreductive surgery (PCS) is an important factor influencing survival for patients with high-grade serous ovarian cancer (HGSC). More research is needed to identify factors associated with having macroscopic residual disease. We analyzed 12 lifestyle and personal exposures known to be related to ovarian cancer risk or inflammation to identify those associated with having residual disease after surgery.This analysis used data on 2054 patients with advanced stage HGSC from the Ovarian Cancer Association Consortium. The exposures were body mass index, breastfeeding, oral contraceptive use, depot-medroxyprogesterone acetate use, endometriosis, first-degree family history of ovarian cancer, incomplete pregnancy, menopausal hormone therapy use, menopausal status, parity, smoking, and tubal ligation. Logistic regression models were fit to assess the association between these exposures and having residual disease following PCS.Menopausal estrogen-only therapy (ET) use was associated with 33% lower odds of having macroscopic residual disease compared to never use (OR = 0.67, 95%CI 0.46-0.97, p = 0.033). Compared to nulliparous women, parous women who did not breastfeed had 36% lower odds of having residual disease (OR = 0.64, 95%CI 0.43-0.94, p = 0.022), while there was no association among parous women who breastfed (OR = 0.90, 95%CI 0.65-1.25, p = 0.53).The association between ET and having no macroscopic residual disease is plausible given a strong underlying biologic hypothesis between this exposure and diagnosis with HGSC. If this or the parity finding is replicated, these factors could be included in risk stratification models to determine whether HGSC patients should receive PCS or neoadjuvant chemotherapy.

Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis

The widespread testing for severe acute respiratory syndrome coronavirus 2 infection has facilitated the use of test-negative designs (TNDs) for modeling coronavirus disease 2019 (COVID-19) vaccination and outcomes. Despite the comprehensive literature on TND, the use of TND in COVID-19 studies is relatively new and calls for robust design and analysis to adapt to a rapidly changing and dynamically evolving pandemic and to account for changes in testing and reporting practices. In this commentary, we aim to draw the attention of researchers to COVID-specific challenges in using TND as we are analyzing data amassed over more than two years of the pandemic. We first review when and why TND works and general challenges in TND studies presented in the literature. We then discuss COVID-specific challenges which have not received adequate acknowledgment but may add to the risk of invalid conclusions in TND studies of COVID-19.

Body mass index (BMI) is linked to amyotrophic lateral sclerosis (ALS) risk and prognosis, but additional research is needed. The aim of this study was to identify whether and when historical changes in BMI occurred in ALS participants, how these longer term trajectories associated with survival, and whether metabolomic profiles provided insight into potential mechanisms.ALS and control participants self-reported body height and weight 10 (reference) and 5 years earlier, and at study entry (diagnosis for ALS participants). Generalized estimating equations evaluated differences in BMI trajectories between cases and controls. ALS survival was evaluated by BMI trajectory group using accelerated failure time models. BMI trajectories and survival associations were explored using published metabolomic profiling and correlation networks.Ten-year BMI trends differed between ALS and controls, with BMI loss in the 5 years before diagnosis despite BMI gains 10 to 5 years beforehand in both groups. An overall 10-year drop in BMI associated with a 27.1% decrease in ALS survival (P = .010). Metabolomic networks in ALS participants showed dysregulation in sphingomyelin, bile acid, and plasmalogen subpathways.ALS participants lost weight in the 5-year period before enrollment. BMI trajectories had three distinct groups and the group with significant weight loss in the past 10 years had the worst survival. Participants with a high BMI and increase in weight in the 10 years before symptom onset also had shorter survival. Certain metabolomics profiles were associated with the BMI trajectories. Replicating these findings in prospective cohorts is warranted.

To identify associations between occupational settings and self-reported occupational exposures on amyotrophic lateral sclerosis (ALS) survival and phenotypes. All patients seen in the University of Michigan Pranger ALS Clinic were invited to complete an exposure assessment querying past occupations and exposures. Standard occupational classification (SOC) codes for each job and the severity of various exposure types were derived. Cox proportional hazards models associated all-cause mortality with occupational settings and the self-reported exposures after adjusting for sex, diagnosis age, revised El Escorial criteria, onset segment, revised ALS Functional Rating Scale (ALSFRS-R), and time from symptom onset to diagnosis. Multinomial logistic regression models with three categories, adjusted for age, assessed the association between occupational settings and exposures to onset segment. Among the 378 ALS participants (median age, 64.7 years; 54.4% male), poorer survival was associated with work in SOC code “Production Occupations” and marginally with work in “Military Occupation”; poor survival associated with self-reported occupational pesticide exposure in adjusted models. Among onset segments: cervical onset was associated with ALS participants having ever worked in “Buildings and Grounds Cleaning and Maintenance Occupations,” “Construction and Extraction Occupations,” and “Production Occupations”; bulbar onset with self-reported occupational exposure to radiation; and cervical onset with exposure to particulate matter, volatile organic compounds, metals, combustion and diesel exhaust, electromagnetic radiation, and radiation. Occupational settings and self-reported exposures influence ALS survival and onset segment. Further studies are needed to explore and understand these relationships, most advantageously using prospective cohorts and detailed ALS registries.

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Since measurements are often unfeasible, Exposure Polygenic Risk Scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks, the Michigan Genomics Initiative and the UK Biobank. We established ExPRS for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially, the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

Background: Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are ubiquitous synthetic chemicals that may disrupt blood pressure controls; however, human evidence to support this hypothesis is scant. We examined the association between serum concentrations of PFAS and risks of developing hypertension. Methods: This study included 1058 midlife women initially free of hypertension from the multiracial and multiethnic SWAN (Study of Women’s Health Across the Nation) with annual follow-up visits between 1999 and 2017. Hypertension was defined as blood pressure ≥140 mm Hg systolic or ≥90 mm Hg diastolic or receiving antihypertensive treatment. Cox proportional hazards models were utilized to calculate hazard ratios and 95% CIs. Quantile g-computation was implemented to evaluate the joint effect of PFAS mixtures. Results: During 11 722 person-years of follow-up, 470 participants developed incident hypertension (40.1 cases per 1000 person-years). Compared with the lowest tertile, women in the highest tertile of baseline serum concentrations had adjusted hazard ratios of 1.42 (95% CI, 1.19–1.68) for perfluorooctane sulfonate ( P trend=0.01), 1.47 (95% CI, 1.24–1.75) for linear perfluorooctanoate ( P trend=0.01), and 1.42 (95% CI, 1.19–1.70) for 2-(N-ethyl-perfluorooctane sulfonamido) acetate ( P trend=0.01). No significant associations were observed for perfluorononanoate and perfluorohexane sulfonate. In the mixture analysis, women in the highest tertile of overall PFAS concentrations had a hazard ratio of 1.71 (95% CI, 1.15–2.54; P trend=0.008), compared with those in the lowest tertile. Conclusions: Several PFAS showed positive associations with incident hypertension. These findings suggest that PFAS might be an underappreciated contributing factor to women’s cardiovascular disease risk.

Motivation In the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution. Results We propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case–control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant–phenotype associations. Availability and implementation The summary statistics and code generated in this study are available at https://github.com/styvon/TAPE. Supplementary information Supplementary data are available at Bioinformatics online.

Background: Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. Results: We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. Conclusions: By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils

Background: Exposure to systemic racism is linked to increased dementia burden. To assess systemic inflammation as a potential pathway linking exposure to racism and dementia disparities, we investigated the mediating role of C-reactive protein (CRP), a systemic inflammation marker, and the moderating role of race/ethnicity on racialized disparities in incident dementia. Methods: In the US Health and Retirement Study (n=5,143), serum CRP was measured at baseline (2006, 2008 waves). Incident dementia was classified by cognitive tests over a six-year follow-up. Self-reported racialized categories were a proxy for exposure to the racialization process. We decomposed racialized disparities in dementia incidence (non-Hispanic Black and/or Hispanic vs. non-Hispanic White) into 1) the mediated effect of CRP, 2) the moderated portion attributable to the interaction between racialized group membership and CRP, and 3) the controlled direct effect (other pathways through which racism operates). Results: The 6-year cumulative incidence of dementia was 15.5%. Among minoritized participants (i.e., non-Hispanic Black and/or Hispanic), high CRP levels (> 75th percentile or 4.57mcg/mL) was associated with 1.27 (95%CI: 1.01,1.59) times greater risk of incident dementia than low CRP (

Complete clinical and molecular data of all samples used in this study.

Purpose:Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with few therapies; however, patients with locoregional disease have variable outcomes. The Cancer Genome Atlas project on ACC (ACC-TCGA) identified that cancers of patients with homogeneously rapidly recurrent or fatal disease bear a unique CpG island hypermethylation phenotype, “CIMP-high.” We sought to identify a biomarker that faithfully captures this subgroup.Experimental Design: We analyzed ACC-TCGA data to characterize differentially regulated biological processes, and identify a biomarker that is methylated and silenced exclusively in CIMP-high ACC. In an independent cohort of 114 adrenocortical tumors (80 treatment-naive primary ACC, 22 adrenocortical adenomas, and 12 non-naive/nonprimary ACC), we evaluated biomarker methylation by a restriction digest/qPCR-based approach, validated by targeted bisulfite sequencing. We evaluated expression of this biomarker and additional prognostic markers by qPCR.Results:We show that CIMP-high ACC is characterized by upregulation of cell cycle and DNA damage response programs, and identify that hypermethylation and silencing of G0S2 distinguishes this subgroup. We confirmed G0S2 hypermethylation and silencing is exclusive to 40% of ACC, and independently predicts shorter disease-free and overall survival (median 14 and 17 months, respectively). Finally, G0S2 methylation combined with validated molecular markers (BUB1B-PINK1) stratifies ACC into three groups, with uniformly favorable, intermediate, and uniformly dismal outcomes.Conclusions:G0S2 hypermethylation is a hallmark of rapidly recurrent or fatal ACC, amenable to targeted assessment using routine molecular diagnostics. Assessing G0S2 methylation is straightforward, feasible for clinical decision-making, and will enable the direction of efficacious adjuvant therapies for patients with aggressive ACC.

This compiled PDF includes Supplementary Tables S3, S4, S6, S7, and S8, as follows: Supp. Table S3. G0S2 hypermethylation predicts CIMP-high. Supp. Table S4. Clinical characteristics of FMUSP+UM ACC and ACA Cohorts. Supp. Table S6. EpiTect accurately measures binary G0S2 methylation status. Supp. Table S7. Hypermethylation and silencing of G0S2 is heterogeneous in recurrent, metastatic, and non-treatment naive carcinomas. Supp. Table S8. BUB1B-PINK1 can predict any history of metastasis in patients with G0S2 Unmethylated ACC.

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The variants c.306+5G>A and c.1865T>A (p.Leu622His) of the DNA repair gene MLH1 occur frequently in Spanish Lynch syndrome families. To understand their ancestral history and clinical effect, we performed functional assays and a penetrance analysis and studied their genetic and geographic origins. Detailed family histories were taken from 29 carrier families. Functional analysis included in silico and in vitro assays at the RNA and protein levels. Penetrance was calculated using a modified segregation analysis adjusted for ascertainment. Founder effects were evaluated by haplotype analysis. The identified MLH1 c.306+5G>A and c.1865T>A (p.Leu622His) variants are absent in control populations and segregate with the disease. Tumors from carriers of both variants show microsatellite instability and loss of expression of the MLH1 protein. The c.306+5G>A variant is a pathogenic mutation affecting mRNA processing. The c.1865T>A (p.Leu622His) variant causes defects in MLH1 expression and stability. For both mutations, the estimated penetrance is moderate (age-cumulative colorectal cancer risk by age 70 of 20.1% and 14.1% for c.306+5G>A and of 6.8% and 7.3% for c.1865T>A in men and women carriers, respectively) in the lower range of variability estimated for other pathogenic Spanish MLH1 mutations. A common haplotype was associated with each of the identified mutations, confirming their founder origin. The ages of c.306+5G>A and c.1865T>A mutations were estimated to be 53 to 122 and 12 to 22 generations, respectively. Our results confirm the pathogenicity, moderate penetrance, and founder origin of the MLH1 c.306+5G>A and c.1865T>A mutations. These findings have important implications for genetic counseling and molecular diagnosis of Lynch syndrome. Cancer Res; 70(19); 7379–91. ©2010 AACR.

Supplementary Table 1 from MLH1 Founder Mutations with Moderate Penetrance in Spanish Lynch Syndrome Families

Supplementary Methods and Supplemental Tables 1-6 from MRE11 Deficiency Increases Sensitivity to Poly(ADP-ribose) Polymerase Inhibition in Microsatellite Unstable Colorectal Cancers

Supplementary Figure 1 from MLH1 Founder Mutations with Moderate Penetrance in Spanish Lynch Syndrome Families

Supplementary Table 2 from MLH1 Founder Mutations with Moderate Penetrance in Spanish Lynch Syndrome Families

Supplementary Methods from MLH1 Founder Mutations with Moderate Penetrance in Spanish Lynch Syndrome Families

Background This study aims to examine the worldwide prevalence of post-coronavirus disease 2019 (COVID-19) condition, through a systematic review and meta-analysis. Methods PubMed, Embase, and iSearch were searched on July 5, 2021 with verification extending to March 13, 2022. Using a random-effects framework with DerSimonian-Laird estimator, we meta-analyzed post-COVID-19 condition prevalence at 28+ days from infection. Results Fifty studies were included, and 41 were meta-analyzed. Global estimated pooled prevalence of post-COVID-19 condition was 0.43 (95% confidence interval [CI], .39–.46). Hospitalized and nonhospitalized patients had estimates of 0.54 (95% CI, .44–.63) and 0.34 (95% CI, .25–.46), respectively. Regional prevalence estimates were Asia (0.51; 95% CI, .37–.65), Europe (0.44; 95% CI, .32–.56), and United States of America (0.31; 95% CI, .21–.43). Global prevalence for 30, 60, 90, and 120 days after infection were estimated to be 0.37 (95% CI, .26–.49), 0.25 (95% CI, .15–.38), 0.32 (95% CI, .14–.57), and 0.49 (95% CI, .40–.59), respectively. Fatigue was the most common symptom reported with a prevalence of 0.23 (95% CI, .17–.30), followed by memory problems (0.14; 95% CI, .10–.19). Conclusions This study finds post-COVID-19 condition prevalence is substantial; the health effects of COVID-19 seem to be prolonged and can exert stress on the healthcare system.

Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This paper considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as “stacked” and “grouped” objective functions. Building on existing work, we (a) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (b) incorporate adaptive shrinkage penalties, (c) compare these methods through simulation, and (d) develop an R package miselect. Simulations demonstrate that the “stacked” approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. Supplementary materials are available online.

False negative rates of severe acute respiratory coronavirus 2 diagnostic tests, together with selection bias due to prioritized testing can result in inaccurate modeling of COVID-19 transmission dynamics based on reported "case" counts. We propose an extension of the widely used Susceptible-Exposed-Infected-Removed (SEIR) model that accounts for misclassification error and selection bias, and derive an analytic expression for the basic reproduction number

Background: There is suggestive evidence that inflammation is related to ovarian cancer survival. However, more research is needed to identify inflammation-related factors that are associated with ovarian cancer survival and to determine their combined effects. Methods: This analysis used pooled data on 8,147 women with invasive epithelial ovarian cancer from the Ovarian Cancer Association Consortium. The prediagnosis inflammation-related exposures of interest included alcohol use; aspirin use; other nonsteroidal anti-inflammatory drug use; body mass index; environmental tobacco smoke exposure; history of pelvic inflammatory disease, polycystic ovarian syndrome, and endometriosis; menopausal hormone therapy use; physical inactivity; smoking status; and talc use. Using Cox proportional hazards models, the relationship between each exposure and survival was assessed in 50% of the data. A weighted inflammation-related risk score (IRRS) was developed, and its association with survival was assessed using Cox proportional hazards models in the remaining 50% of the data. Results: There was a statistically significant trend of increasing risk of death per quartile of the IRRS [HR = 1.09; 95% confidence interval (CI), 1.03–1.14]. Women in the upper quartile of the IRRS had a 31% higher death rate compared with the lowest quartile (95% CI, 1.11–1.54). Conclusions: A higher prediagnosis IRRS was associated with an increased mortality risk after an ovarian cancer diagnosis. Further investigation is warranted to evaluate whether postdiagnosis exposures are also associated with survival. Impact: Given that pre- and postdiagnosis exposures are often correlated and many are modifiable, our study results can ultimately motivate the development of behavioral recommendations to enhance survival among patients with ovarian cancer.

Objective To evaluate the association between hysterectomy and ovarian cancer, and to understand how hormone therapy (HT) use and endometriosis affect this association. Methods We conducted a pooled analysis of self-reported data from 11 case-control studies in the Ovarian Cancer Association Consortium (OCAC). Women with (n = 5350) and without ovarian cancer (n = 7544) who never used HT or exclusively used either estrogen-only therapy (ET) or estrogen+progestin therapy (EPT) were included. Risk of invasive epithelial ovarian cancer adjusted for duration of ET and EPT use and stratified on history of endometriosis was determined using odds ratios (ORs) with 95% confidence intervals (CIs). Results Overall and among women without endometriosis, there was a positive association between ovarian cancer risk and hysterectomy (OR = 1.19, 95% CI 1.09-1.31 and OR = 1.20, 95% CI 1.09-1.32, respectively), but no association upon adjusting for duration of ET and EPT use (OR = 1.04, 95% CI 0.94-1.16 and OR = 1.06, 95% CI 0.95-1.18, respectively). Among women with a history of endometriosis, there was a slight inverse association between hysterectomy and ovarian cancer risk (OR = 0.93, 95% CI 0.69-1.26), but this association became stronger and statistically significant after adjusting for duration of ET and EPT use (OR = 0.69, 95% CI 0.48-0.99). Conclusions The hysterectomy-ovarian cancer association is complex and cannot be understood without considering duration of ET and EPT use and history of endometriosis. Failure to take these exposures into account in prior studies casts doubt on their conclusions. Overall, hysterectomy is not risk-reducing for ovarian cancer, however the inverse association among women with endometriosis warrants further investigation.

Phthalates, ubiquitous endocrine-disrupting chemicals, may affect ovarian folliculogenesis and steroidogenesis. We examined the associations of urinary phthalate metabolites with hormones including estradiol, testosterone, follicle-stimulating hormone (FSH), sex hormone–binding globulin (SHBG), and anti-Müllerian hormone (AMH), and timing of natural menopause in midlife women. Data were from 1189 multiracial/multiethnic women aged 45 to 56 years without hormone therapy from the Study of Women's Health Across the Nation (SWAN). Urinary concentrations of 12 phthalate metabolites and hormones were repeatedly measured in 1999 to 2000 and 2002 to 2003, resulting in a total of 2111 observations. Linear mixed-effect models were used to calculate percentage differences (%D) and 95% CIs in serum concentrations of estradiol, testosterone, FSH, SHBG, and AMH. Cox proportional-hazards models were used to calculate hazard ratios (HRs) and 95% CIs of natural menopause. We observed statistically significant associations of phthalate metabolites with lower testosterone concentrations: MCOP with testosterone (%D: −2.08%; 95% CI, −3.66 to −0.47) and MnBP with testosterone (%D: −1.99%; 95% CI, −3.82 to −0.13), after adjusting for multiple comparisons with false discovery rates less than 5%. Lower AMH concentrations were also found with higher MECPP (%D: −14.26%; 95% CI, −24.10 to −3.14), MEHHP (%D: −15.58%; 95% CI, −24.59 to −5.50), and MEOHP (%D: −13.50%; 95% CI, −22.93 to −2.90). No associations were observed for other hormones or timing of natural menopause. These results suggest that exposure to phthalates may affect circulating levels of testosterone and diminish the ovarian reserve in midlife women. Given the widespread exposure, reduced exposure to phthalates may be a key step to prevent reproductive effects of phthalates.

BackgroundTraditional risk factors including demographics, blood pressure, cholesterol, and diabetes status are successfully able to predict a proportion of cardiovascular disease (CVD) events. Whether including additional routinely measured factors improves CVD prediction is unclear. To determine whether a comprehensive risk factor list, including clinical blood measures, blood counts, anthropometric measures, and lifestyle factors, improves prediction of CVD deaths beyond traditional factors.MethodsThe analysis comprised of 21,982 participants aged 40 years and older (mean age=59.4 years at baseline) from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2016 survey cycles. Data were linked with the National Death Index mortality data through 2019 and split into 80:20 training and testing sets. Relative to the traditional risk factors (age, sex, race/ethnicity, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, antihypertensive medications, and diabetes), we compared models with an additional 22 clinical blood biomarkers, 20 complete blood counts, 7 anthropometric measures, 51 dietary factors, 13 cardiovascular health-related questions, and all 113 predictors together. To build prediction models for CVD mortality, we performed Cox proportional hazards regression, elastic-net (ENET) penalized Cox regression, and random survival forest, and compared classification using C-index and net reclassification improvement.ResultsDuring follow-up (median, 9.3 years), 3,075 participants died; 30.9% (1,372/3,075) deaths were from cardiovascular causes. In Cox proportional hazards models with traditional risk factors (C-index=0.850), CVD mortality classification improved with incorporation of clinical blood biomarkers (C-index=0.867), blood counts (C-index=0.861), and all predictors (C-index=0.871). Net CVD mortality reclassification improved 13.2% by adding clinical blood biomarkers and 12.2% by adding all predictors. Results for ENET-penalized Cox regression and random survival forest were similar. No improvement was observed in separate models for anthropometric measures, dietary nutrient intake, or cardiovascular health-related questions.ConclusionsThe addition of clinical blood biomarkers and blood counts substantially improves CVD mortality prediction, beyond traditional risk factors. These biomarkers may serve as an important clinical and public health screening tool for the prevention of CVD deaths.Clinical PerspectiveWhat is new?We tested the predictive value of a combination of 113 potential predictors, including 22 clinical blood biomarkers, 20 complete blood counts, 7 anthropometric measures, 51 dietary factors, and 13 cardiovascular health-related questions, beyond traditional risk factors, for CVD mortality in adults in the United States.The addition of predictors, specifically blood biomarkers such as glucose, uric acid, bicarbonate, urea nitrogen, total protein, creatinine, calcium, globulin, and phosphorus, improved CVD mortality prediction.What are the clinical implications?Accurate prediction of CVD mortality is essential for identifying those at risk and targeting interventions.Our findings highlight the clinical translational utility of predictors, including the biomarkers already well established and routinely applied in clinical practice, for CVD mortality prediction.

Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modelling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms.

Timely diagnostic testing for active SARS‐CoV‐2 viral infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies with limited resources. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral diagnostic tests. The framework accounts for imperfect test results, selective testing in certain high‐risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infections, but can also be used for long‐time surveillance to detect new outbreaks. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID‐19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of diagnostic tests with different costs and accuracy, for example, the RT‐PCR and the rapid antigen test (RAT), under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision‐making at a community level and adapting testing plans to different stages of an epidemic. The conceptual framework has broader relevance beyond the current COVID‐19 pandemic.